our understanding of the regression analysis is correct. Let's break down each component: B values: These are the regression coefficients. A positive B value indicates a positive relationship between the independent and dependent variables, while a negative B value indicates a negative relationship. R Square (.3982): This is the coefficient of determination. It tells us that approximately 39.82% of the variance in the dependent variable (STRATEGIES) can be explained by the independent variables (STRATEGIC and EFFECTIVEPROJ). Adjusted R Square (.428P): This is a more accurate measure of the goodness of fit of the model as it adjusts for the number of predictors. The 'P' here seems out of place as Adjusted R Square is usually a value between 0 and 1. If it's a typo and the value is .428, it means that after adjusting for the number of predictors, about 42.8% of the variance in STRATEGIES can be explained. Std. Error of the Estimate: This is a measure of the standard deviation of the residuals. It gives us an idea of how much our predictions deviate, on average, from the actual values in our dataset. Durbin-Watson statistic (2.026): This tests for autocorrelation in the residuals. A value of 2.026 is close to 2, suggesting that there is no significant autocorrelation in the data